Java 10, 405 ...didn't fit anymore; see edit history.. 316 315 bytes

P->{int n=P.length,l=0,i=0,p,q,t[],h[][]=P.clone(),s=0;for(;++i<n;)l=P[i][0]<P[l][0]?i:l;p=l;do for(h[s++]=P[p],q=-~p%n,i=n;i-->0;q=(t[1]-P[p][1])*(P[q][0]-t[0])<(t[0]-P[p][0])*(P[q][1]-t[1])?i:q)t=P[i];while((p=q)!=l);for(p=i=0;i<s;p-=(t[0]+h[++i%s][0])*(t[1]-h[i%s][1]))t=h[i];return Math.round(.5*p/~(p%=2))*~p;}

-52 bytes thanks to @OlivierGrégoire
-3 bytes thanks to @PeterTaylor
-8 bytes thanks to @ceilingcat

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Or 298 bytes without rounding...

Explanation:

There are three steps to do:

1. Calculate the points for the Convex Hull based on the input-coordinates (using Jarvis' Algorithm/Wrapping)
2. Calculate the area of this Convex Hull
3. Banker's rounding..

To calculate the coordinates that are part of the Convex Hull, we use the following approach:

Set point \$l\$ and \$p\$ to the left-most coordinate. Then calculate the next point \$p\$ in a counterclockwise rotation; and continue doing so until we've reached back at the initial point \$l\$. Here a visual for this:

As for the code:

P->{                      // Method with 2D integer array as parameter & long return-type
  int n=P.length,         //  Integer `n`, the amount of points in the input
      l=0,                //  Integer `l`, to calculate the left-most point
      i=0,                //  Index-integer `i`
      p,                  //  Integer `p`, which will be every next counterclockwise point
      q,                  //  Temp integer `q`
      t[],                //  Temp integer-array/point
      h[][]=P.clone(),    //  Initialize an array of points `h` for the Convex Hull
      s=0;                //  And a size-integer for this Convex Hull array, starting at 0
  for(;++i<n;)            //  Loop `i` in the range [1, `n`):
    l=                    //   Change `l` to:
      P[i][0]<P[l][0]?    //   If i.x is smaller than l.x:
       i                  //    Replace `l` with the current `i`
      :l;                 //   Else: leave `l` unchanged
  p=l;                    //  Now set `p` to this left-most coordinate `l`
  do                      //  Do:
    for(h[s++]=P[p],      //   Add the `p`'th point to the 2D-array `h`
        q=-~p%n,          //   Set `q` to `(p+1)` modulo-`n`
        i=n;i-->0;        //    Loop `i` in the range (`n`, 0]:
        ;q=               //      After every iteration: change `q` to:
                          //       We calculate: (i.y-p.y)*(q.x-i.x)-(i.x-p.x)*(q.y-i.y), 
                          //       which results in 0 if the three points are collinear;
                          //       a positive value if they are clockwise;
                          //       or a negative value if they are counterclockwise
           (t[1]-P[p][1])*(P[q][0]-t[0])<(t[0]-P[p][0])*(P[q][1]-t[1])?
                          //       So if the three points are counterclockwise:
            i             //        Replace `q` with `i`
           :q)            //       Else: leave `q` unchanged
      t=P[i];             //     Set `t` to the `i`'th Point (to save bytes)
  while((p=q)             //  And after every while-iteration: replace `p` with `q`
             !=l);        //  Continue the do-while as long as `p` is not back at the
                          //  left-most point `l` yet
  // Now step 1 is complete, and we have our Convex Hull points in the List `h`
                  
  for(p=i=0;              //  Set `p` (the area) to 0
      i<s                 //  Loop `i` in the range [0, `s`):
      ;p-=                //    After every iteration: Decrease the area `p` by:
        (t[0]+h[++i%s][0])//     i.x+(i+1).x
        *(t[1]-h[i%s][1]))//     Multiplied by i.y-(i+1).y
    t=h[i];               //   Set `t` to the `i`'th point (to save bytes)
 return Math.round(.5*p/~(p%=2))*~p;}
                          //  And return `p/2` rounded to integer with half-even

[R + sp package], 55 bytes

function(x)round(sp::Polygon(x[chull(x),,drop=F])@area)

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A function which takes a n x 2 matrix and returns the rounded area. This uses the sp package. The drop=F is needed to handle the one co-ordinate case. RDRR used for demo since TIO lacks the sp package.

JavaScript (ES6),  191  189 bytes

Implements the Jarvis march (aka gift wrapping algorithm).

P=>(r=(g=p=>([X,Y]=P[p],Y*h-X*v)+(P.map(([x,y],i)=>q=(y-Y)*(P[q][0]-x)<(x-X)*(P[q][1]-y)?i:q,q=P[++p]?p:0,h=X,v=Y)|q?g(q):V*h-H*v))(v=h=0,([[H,V]]=P.sort(([x],[X])=>x-X)))/2)+(r%1&&r&1)/2|0

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Or 170 bytes without the cumbersome rounding scheme.

R, 85 81 78 bytes

function(i,h=chull(i),j=c(h,h[1]))round((i[h,1]+i[j[-1],1])%*%diff(-i[j,2])/2)

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Takes input as a 2-column matrix - first for x, second for y. R's round actually uses banker's rounding method, so we are quite lucky here.

The code uses a built-in function to determine, which points form the convex hull, and then applies the standard formula \$\sum_{i}{(x_{i-1}+x)\cdot(y_{i-1}-y_i)}/2\$ to get the polygon surface area.

Thanks to Giuseppe for -3 bytes.

Wolfram Language (Mathematica), 27 bytes

Round@*Area@*ConvexHullMesh

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SQL Server 2012+, 84 bytes

SELECT Round(Geometry::ConvexHullAggregate(Geometry::Point(x,y,0)).STArea(),0)FROM A

Makes use of the geometry functions and aggregates in SQL Server. Coordindates are from table A with columns x and y.